The present invention relates to model blending, and more specifically, to parameter-dependent model-blending with multi-expert based machine learning and proxy sites.
Physical models that are based on principles of physics and chemistry and which are used to forecast parameters or conditions in a wide variety of arenas are known. Meteorological models may be used to forecast weather, for example. These models may include input parameters such as pressure, temperature, and wind velocity and provide estimates or predictions of output parameters. Corrosion models may forecast pipeline corrosion, as another example. These models may include input parameters such as temperature, gas concentrations, pressure, and flow conditions. Different physical models that provide the same predicted output condition or parameter may be blended to improve the prediction offered by any one of the models individually.